{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "362d4e42",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:13.117545Z",
     "iopub.status.busy": "2022-08-26T06:34:13.116605Z",
     "iopub.status.idle": "2022-08-26T06:34:14.523275Z",
     "shell.execute_reply": "2022-08-26T06:34:14.522036Z"
    },
    "papermill": {
     "duration": 1.417392,
     "end_time": "2022-08-26T06:34:14.526180",
     "exception": false,
     "start_time": "2022-08-26T06:34:13.108788",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from time import time\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "\n",
    "from tspiral.forecasting import ForecastingCascade, ForecastingChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb3c74b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:14.540541Z",
     "iopub.status.busy": "2022-08-26T06:34:14.539655Z",
     "iopub.status.idle": "2022-08-26T06:34:14.555137Z",
     "shell.execute_reply": "2022-08-26T06:34:14.554094Z"
    },
    "papermill": {
     "duration": 0.025484,
     "end_time": "2022-08-26T06:34:14.557693",
     "exception": false,
     "start_time": "2022-08-26T06:34:14.532209",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### UTILITY FUNCTIONS TO SIMULATE TIME SERIES AND STORE FORECASTING METRICS ###\n",
    "\n",
    "def gen_sinusoidal(timesteps, amp, freq, noise):\n",
    "    X = np.arange(timesteps)\n",
    "    e = np.random.normal(0,noise, (timesteps,))\n",
    "    y = amp*np.sin(X*(2*np.pi/freq))+e\n",
    "    return y\n",
    "\n",
    "def gen_randomwalk(timesteps, noise):\n",
    "    y = np.random.normal(0,noise, (timesteps,))\n",
    "    return y.cumsum()\n",
    "\n",
    "def gen_ts(timesteps, amp, freq, noise, random_state=0):\n",
    "    np.random.seed(random_state)\n",
    "    \n",
    "    if isinstance(freq, (int,float)):\n",
    "        seas = gen_sinusoidal(timesteps=timesteps, amp=amp, freq=freq, noise=noise)\n",
    "    elif np.iterable(freq) and not isinstance(freq, str):\n",
    "        seas = np.zeros(timesteps)\n",
    "        for f in freq:\n",
    "            if isinstance(f, (int,float)):\n",
    "                seas += \\\n",
    "                    gen_sinusoidal(timesteps=timesteps, amp=amp, freq=f, noise=noise)\n",
    "            else:\n",
    "                raise ValueError(\"freq not understood.\")\n",
    "    else:\n",
    "        raise ValueError(\"freq not understood.\")\n",
    "    \n",
    "    rw = gen_randomwalk(timesteps=timesteps, noise=1)\n",
    "    X = np.linspace(0,10, timesteps).reshape(-1,1)\n",
    "    X = np.power(X, [1,2])\n",
    "    trend = LinearRegression().fit(X, rw).predict(X)\n",
    "    \n",
    "    return seas + trend\n",
    "\n",
    "\n",
    "def get_metrics(model, X, y, metrics=None):\n",
    "    \n",
    "    score = {\n",
    "        'mse': [model.score(X, y, scoring='mse')],\n",
    "        'mae': [model.score(X, y, scoring='mae')],\n",
    "        'mape': [model.score(X, y, scoring='mape')],\n",
    "        'rmse': [model.score(X, y, scoring='rmse')],\n",
    "    }\n",
    "    \n",
    "    if metrics is not None:\n",
    "        for metric,s in score.items():\n",
    "            metrics[metric].extend(s)\n",
    "    else:\n",
    "        metrics = score\n",
    "    \n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88597934",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:14.571945Z",
     "iopub.status.busy": "2022-08-26T06:34:14.571228Z",
     "iopub.status.idle": "2022-08-26T06:34:14.807242Z",
     "shell.execute_reply": "2022-08-26T06:34:14.805908Z"
    },
    "papermill": {
     "duration": 0.246158,
     "end_time": "2022-08-26T06:34:14.809861",
     "exception": false,
     "start_time": "2022-08-26T06:34:14.563703",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 100)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### SIMULATE SYNTHETIC TIME SERIES ### \n",
    "\n",
    "n_series, timesteps = 100, 4_000\n",
    "\n",
    "df = {}\n",
    "for i in range(n_series):\n",
    "    df[f'ts_{i}'] = gen_ts(\n",
    "        timesteps, \n",
    "        amp=10, freq=[24,24*7], noise=4,\n",
    "        random_state=i\n",
    "    )\n",
    "df = pd.DataFrame(\n",
    "    df, index=pd.date_range(\n",
    "    '2020-01-01', periods=timesteps, freq='H'\n",
    "    )\n",
    ")\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5996fb10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:14.823771Z",
     "iopub.status.busy": "2022-08-26T06:34:14.823330Z",
     "iopub.status.idle": "2022-08-26T06:34:15.370874Z",
     "shell.execute_reply": "2022-08-26T06:34:15.369424Z"
    },
    "papermill": {
     "duration": 0.558951,
     "end_time": "2022-08-26T06:34:15.374886",
     "exception": false,
     "start_time": "2022-08-26T06:34:14.815935",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT SYNTHETIC TIME SERIES ###\n",
    "\n",
    "df.sample(n=10, axis=1, random_state=33).plot(\n",
    "    legend=False, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86758b02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:15.392811Z",
     "iopub.status.busy": "2022-08-26T06:34:15.392356Z",
     "iopub.status.idle": "2022-08-26T06:34:15.406869Z",
     "shell.execute_reply": "2022-08-26T06:34:15.405601Z"
    },
    "papermill": {
     "duration": 0.02649,
     "end_time": "2022-08-26T06:34:15.409460",
     "exception": false,
     "start_time": "2022-08-26T06:34:15.382970",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3832, 1), (168, 1), (3832, 100), (168, 100))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### TRAIN TEST SPLIT ###\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(np.zeros((timesteps,1)), df, test_size=24*7, shuffle=False)\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8feeda17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:15.428063Z",
     "iopub.status.busy": "2022-08-26T06:34:15.427338Z",
     "iopub.status.idle": "2022-08-26T06:34:15.431330Z",
     "shell.execute_reply": "2022-08-26T06:34:15.430552Z"
    },
    "papermill": {
     "duration": 0.016258,
     "end_time": "2022-08-26T06:34:15.433591",
     "exception": false,
     "start_time": "2022-08-26T06:34:15.417333",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### INTIALIZE EMPTY DICT TO STORE RESULTS ###\n",
    "\n",
    "results = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba2464de",
   "metadata": {
    "papermill": {
     "duration": 0.007706,
     "end_time": "2022-08-26T06:34:15.449830",
     "exception": false,
     "start_time": "2022-08-26T06:34:15.442124",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Recursive Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69fc0145",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:15.468152Z",
     "iopub.status.busy": "2022-08-26T06:34:15.466946Z",
     "iopub.status.idle": "2022-08-26T06:34:20.767426Z",
     "shell.execute_reply": "2022-08-26T06:34:20.763152Z"
    },
    "papermill": {
     "duration": 5.315843,
     "end_time": "2022-08-26T06:34:20.773575",
     "exception": false,
     "start_time": "2022-08-26T06:34:15.457732",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 71.55it/s]\n"
     ]
    }
   ],
   "source": [
    "### SELECT MEANINGFUL LAGS ###\n",
    "\n",
    "supports = []\n",
    "\n",
    "model = ForecastingCascade(\n",
    "    make_pipeline(\n",
    "        SelectFromModel(\n",
    "            Ridge(), \n",
    "            threshold='median',\n",
    "            max_features=72,\n",
    "        ), \n",
    "        Ridge()\n",
    "    ),\n",
    "    lags=range(1,24*7+1),\n",
    "    groups=[0],\n",
    ")\n",
    "\n",
    "for c in tqdm(df.columns):\n",
    "    \n",
    "    model.fit(X_train, y_train[c])\n",
    "    supports.append(\n",
    "        model.estimator_['selectfrommodel'].get_support(indices=True)\n",
    "    )\n",
    "    \n",
    "supports = np.asarray(supports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87d10602",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:20.872811Z",
     "iopub.status.busy": "2022-08-26T06:34:20.871078Z",
     "iopub.status.idle": "2022-08-26T06:34:48.841243Z",
     "shell.execute_reply": "2022-08-26T06:34:48.840193Z"
    },
    "papermill": {
     "duration": 28.020303,
     "end_time": "2022-08-26T06:34:48.845091",
     "exception": false,
     "start_time": "2022-08-26T06:34:20.824788",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:25<00:00,  3.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mse': 48.05708833491028, 'mae': 5.481918672496965, 'mape': 0.9726851741897634, 'rmse': 6.764016717736322, 'tot time': 4.706166982650757, 'std time': 0.014753314409772315}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "### FORECASTING WITH ALL LAGS ###\n",
    "\n",
    "times = []\n",
    "metrics = None\n",
    "\n",
    "for i,c in tqdm(enumerate(df.columns), total=n_series):\n",
    "    \n",
    "    model = ForecastingCascade(\n",
    "        Ridge(),\n",
    "        lags=range(1,24*7+1),\n",
    "        groups=[0],\n",
    "    )\n",
    "    model.fit(X_train, y_train[c])\n",
    "    init = time()\n",
    "    pred = model.predict(X_test)\n",
    "    total_time = time() - init\n",
    "    times.append(total_time)\n",
    "    \n",
    "    metrics = get_metrics(model, X_test, y_test[c], metrics)\n",
    "\n",
    "\n",
    "metrics = {m:np.mean(s) for m,s in metrics.items()}\n",
    "metrics['tot time'] = np.sum(times)\n",
    "metrics['std time'] = np.std(times)\n",
    "results['recursive full'] = metrics\n",
    "\n",
    "print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e5650fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:48.882168Z",
     "iopub.status.busy": "2022-08-26T06:34:48.881198Z",
     "iopub.status.idle": "2022-08-26T06:34:50.178048Z",
     "shell.execute_reply": "2022-08-26T06:34:50.176825Z"
    },
    "papermill": {
     "duration": 1.319954,
     "end_time": "2022-08-26T06:34:50.182965",
     "exception": false,
     "start_time": "2022-08-26T06:34:48.863011",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 59.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mse': 67.29899591254771, 'mae': 6.4903461198831955, 'mape': 1.2991695997702812, 'rmse': 7.9622699680720155, 'tot time': 0.2198929786682129, 'std time': 0.00416511117185989}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "### FORECASTING WITH DUMMY LAGS SELECTION ###\n",
    "\n",
    "times = []\n",
    "metrics = None\n",
    "\n",
    "for i,c in tqdm(enumerate(df.columns), total=n_series):\n",
    "    \n",
    "    model = ForecastingCascade(\n",
    "        Ridge(),\n",
    "        lags=range(24,24*8,24),\n",
    "        groups=[0],\n",
    "    )\n",
    "    model.fit(X_train, y_train[c])\n",
    "    init = time()\n",
    "    pred = model.predict(X_test)\n",
    "    total_time = time() - init\n",
    "    times.append(total_time)\n",
    "    \n",
    "    metrics = get_metrics(model, X_test, y_test[c], metrics)\n",
    "\n",
    "\n",
    "metrics = {m:np.mean(s) for m,s in metrics.items()}\n",
    "metrics['tot time'] = np.sum(times)\n",
    "metrics['std time'] = np.std(times)\n",
    "results['recursive dummy'] = metrics\n",
    "\n",
    "print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c145ba4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:34:50.274857Z",
     "iopub.status.busy": "2022-08-26T06:34:50.274167Z",
     "iopub.status.idle": "2022-08-26T06:35:09.750319Z",
     "shell.execute_reply": "2022-08-26T06:35:09.749153Z"
    },
    "papermill": {
     "duration": 19.525914,
     "end_time": "2022-08-26T06:35:09.753559",
     "exception": false,
     "start_time": "2022-08-26T06:34:50.227645",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:16<00:00,  6.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mse': 48.979764637745184, 'mae': 5.530049153877801, 'mape': 1.0139963306517865, 'rmse': 6.811105320721744, 'tot time': 3.093308210372925, 'std time': 0.009246940504899508}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "### FORECASTING WITH SELECTED LAGS ###\n",
    "\n",
    "times = []\n",
    "metrics = None\n",
    "\n",
    "for i,c in tqdm(enumerate(df.columns), total=n_series):\n",
    "\n",
    "    model = ForecastingCascade(\n",
    "        Ridge(),\n",
    "        lags=supports[i] +1,\n",
    "        groups=[0],\n",
    "    )\n",
    "    model.fit(X_train, y_train[c])\n",
    "    init = time()\n",
    "    pred = model.predict(X_test)\n",
    "    total_time = time() - init\n",
    "    times.append(total_time)\n",
    "    \n",
    "    metrics = get_metrics(model, X_test, y_test[c], metrics)\n",
    "\n",
    "\n",
    "metrics = {m:np.mean(s) for m,s in metrics.items()}\n",
    "metrics['tot time'] = np.sum(times)\n",
    "metrics['std time'] = np.std(times)\n",
    "results['recursive filtered'] = metrics\n",
    "\n",
    "print(metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "103b215b",
   "metadata": {
    "papermill": {
     "duration": 0.023538,
     "end_time": "2022-08-26T06:35:09.800649",
     "exception": false,
     "start_time": "2022-08-26T06:35:09.777111",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Direct Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36a47b11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:35:09.851256Z",
     "iopub.status.busy": "2022-08-26T06:35:09.850807Z",
     "iopub.status.idle": "2022-08-26T06:37:26.261150Z",
     "shell.execute_reply": "2022-08-26T06:37:26.260250Z"
    },
    "papermill": {
     "duration": 136.438575,
     "end_time": "2022-08-26T06:37:26.263279",
     "exception": false,
     "start_time": "2022-08-26T06:35:09.824704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [01:30<00:00,  1.11it/s]\n"
     ]
    }
   ],
   "source": [
    "### SELECT MEANINGFUL LAGS ###\n",
    "\n",
    "supports = []\n",
    "\n",
    "model = ForecastingChain(\n",
    "    make_pipeline(\n",
    "        SelectFromModel(\n",
    "            Ridge(), threshold='median',\n",
    "        ), \n",
    "        Ridge()\n",
    "    ),\n",
    "    n_estimators=24*7,\n",
    "    lags=range(1,24*7+1),\n",
    "    groups=[0],\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "for c in tqdm(df.columns):\n",
    "    \n",
    "    model.fit(X_train, y_train[c])\n",
    "    supports.append(\n",
    "        np.argsort(np.asarray([\n",
    "            est.estimator_['selectfrommodel'].get_support() \n",
    "            for est in model.estimators_\n",
    "        ]).sum(0))[-72:]\n",
    "    )\n",
    "    \n",
    "supports = np.asarray(supports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c96da1f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:37:26.324624Z",
     "iopub.status.busy": "2022-08-26T06:37:26.323890Z",
     "iopub.status.idle": "2022-08-26T06:40:06.603031Z",
     "shell.execute_reply": "2022-08-26T06:40:06.601403Z"
    },
    "papermill": {
     "duration": 160.314504,
     "end_time": "2022-08-26T06:40:06.607270",
     "exception": false,
     "start_time": "2022-08-26T06:37:26.292766",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [01:31<00:00,  1.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mse': 39.46550343227865, 'mae': 5.010466185853994, 'mape': 0.7879850302925503, 'rmse': 6.247994700693516, 'tot time': 7.232915878295898, 'std time': 0.00648400481762101}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "### FORECASTING WITH ALL LAGS ###\n",
    "\n",
    "times = []\n",
    "metrics = None\n",
    "\n",
    "for i,c in tqdm(enumerate(df.columns), total=n_series):\n",
    "\n",
    "    model = ForecastingChain(\n",
    "        Ridge(),\n",
    "        n_estimators=24*7,\n",
    "        lags=range(1,24*7+1),\n",
    "        groups=[0],\n",
    "        n_jobs=-1\n",
    "    )\n",
    "    model.fit(X_train, y_train[c])\n",
    "    init = time()\n",
    "    pred = model.predict(X_test)\n",
    "    total_time = time() - init\n",
    "    times.append(total_time)\n",
    "    \n",
    "    metrics = get_metrics(model, X_test, y_test[c], metrics)\n",
    "\n",
    "\n",
    "metrics = {m:np.mean(s) for m,s in metrics.items()}\n",
    "metrics['tot time'] = np.sum(times)\n",
    "metrics['std time'] = np.std(times)\n",
    "results['direct full'] = metrics\n",
    "\n",
    "print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f833be1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:40:06.736473Z",
     "iopub.status.busy": "2022-08-26T06:40:06.736073Z",
     "iopub.status.idle": "2022-08-26T06:40:09.189421Z",
     "shell.execute_reply": "2022-08-26T06:40:09.188101Z"
    },
    "papermill": {
     "duration": 2.494219,
     "end_time": "2022-08-26T06:40:09.193098",
     "exception": false,
     "start_time": "2022-08-26T06:40:06.698879",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:03<00:00, 30.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mse': 74.06162591691067, 'mae': 6.772299542791172, 'mape': 1.4183816959108935, 'rmse': 8.245286437797434, 'tot time': 0.33985066413879395, 'std time': 0.005219253899677394}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "### FORECASTING WITH DUMMY LAGS SELECTION ###\n",
    "\n",
    "times = []\n",
    "metrics = None\n",
    "\n",
    "for i,c in tqdm(enumerate(df.columns), total=n_series):\n",
    "\n",
    "    model = ForecastingChain(\n",
    "        Ridge(),\n",
    "        n_estimators=7,\n",
    "        lags=range(24,24*8,24),\n",
    "        groups=[0],\n",
    "        n_jobs=-1\n",
    "    )\n",
    "    model.fit(X_train, y_train[c])\n",
    "    init = time()\n",
    "    pred = model.predict(X_test)\n",
    "    total_time = time() - init\n",
    "    times.append(total_time)\n",
    "    \n",
    "    metrics = get_metrics(model, X_test, y_test[c], metrics)\n",
    "\n",
    "\n",
    "metrics = {m:np.mean(s) for m,s in metrics.items()}\n",
    "metrics['tot time'] = np.sum(times)\n",
    "metrics['std time'] = np.std(times)\n",
    "results['direct dummy'] = metrics\n",
    "\n",
    "print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15a7be63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:40:09.271955Z",
     "iopub.status.busy": "2022-08-26T06:40:09.271560Z",
     "iopub.status.idle": "2022-08-26T06:41:46.973427Z",
     "shell.execute_reply": "2022-08-26T06:41:46.969049Z"
    },
    "papermill": {
     "duration": 97.752584,
     "end_time": "2022-08-26T06:41:46.984981",
     "exception": false,
     "start_time": "2022-08-26T06:40:09.232397",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:42<00:00,  2.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mse': 41.63313313835895, 'mae': 5.150969410162327, 'mape': 0.8671751363789949, 'rmse': 6.412714467670177, 'tot time': 4.0048534870147705, 'std time': 0.01475796655667789}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "### FORECASTING WITH SELECTED LAGS ###\n",
    "\n",
    "times = []\n",
    "metrics = None\n",
    "\n",
    "for i,c in tqdm(enumerate(df.columns), total=n_series):\n",
    "    \n",
    "    model = ForecastingChain(\n",
    "        Ridge(),\n",
    "        n_estimators=round(24*7 / (supports[i].min() +1)), \n",
    "        lags=supports[i] +1, \n",
    "        groups=[0],\n",
    "        n_jobs=-1\n",
    "    )\n",
    "    model.fit(X_train, y_train[c])\n",
    "    init = time()\n",
    "    pred = model.predict(X_test)\n",
    "    total_time = time() - init\n",
    "    times.append(total_time)\n",
    "    \n",
    "    metrics = get_metrics(model, X_test, y_test[c], metrics)\n",
    "\n",
    "\n",
    "metrics = {m:np.mean(s) for m,s in metrics.items()}\n",
    "metrics['tot time'] = np.sum(times)\n",
    "metrics['std time'] = np.std(times)\n",
    "results['direct filtered'] = metrics\n",
    "\n",
    "print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de50f77d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:41:47.131565Z",
     "iopub.status.busy": "2022-08-26T06:41:47.131167Z",
     "iopub.status.idle": "2022-08-26T06:41:47.153539Z",
     "shell.execute_reply": "2022-08-26T06:41:47.152245Z"
    },
    "papermill": {
     "duration": 0.069781,
     "end_time": "2022-08-26T06:41:47.156052",
     "exception": false,
     "start_time": "2022-08-26T06:41:47.086271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>mae</th>\n",
       "      <th>mape</th>\n",
       "      <th>rmse</th>\n",
       "      <th>tot time</th>\n",
       "      <th>std time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>recursive full</th>\n",
       "      <td>48.057088</td>\n",
       "      <td>5.481919</td>\n",
       "      <td>0.972685</td>\n",
       "      <td>6.764017</td>\n",
       "      <td>4.706167</td>\n",
       "      <td>0.014753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recursive dummy</th>\n",
       "      <td>67.298996</td>\n",
       "      <td>6.490346</td>\n",
       "      <td>1.299170</td>\n",
       "      <td>7.962270</td>\n",
       "      <td>0.219893</td>\n",
       "      <td>0.004165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recursive filtered</th>\n",
       "      <td>48.979765</td>\n",
       "      <td>5.530049</td>\n",
       "      <td>1.013996</td>\n",
       "      <td>6.811105</td>\n",
       "      <td>3.093308</td>\n",
       "      <td>0.009247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direct full</th>\n",
       "      <td>39.465503</td>\n",
       "      <td>5.010466</td>\n",
       "      <td>0.787985</td>\n",
       "      <td>6.247995</td>\n",
       "      <td>7.232916</td>\n",
       "      <td>0.006484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direct dummy</th>\n",
       "      <td>74.061626</td>\n",
       "      <td>6.772300</td>\n",
       "      <td>1.418382</td>\n",
       "      <td>8.245286</td>\n",
       "      <td>0.339851</td>\n",
       "      <td>0.005219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direct filtered</th>\n",
       "      <td>41.633133</td>\n",
       "      <td>5.150969</td>\n",
       "      <td>0.867175</td>\n",
       "      <td>6.412714</td>\n",
       "      <td>4.004853</td>\n",
       "      <td>0.014758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mse       mae      mape      rmse  tot time  \\\n",
       "recursive full      48.057088  5.481919  0.972685  6.764017  4.706167   \n",
       "recursive dummy     67.298996  6.490346  1.299170  7.962270  0.219893   \n",
       "recursive filtered  48.979765  5.530049  1.013996  6.811105  3.093308   \n",
       "direct full         39.465503  5.010466  0.787985  6.247995  7.232916   \n",
       "direct dummy        74.061626  6.772300  1.418382  8.245286  0.339851   \n",
       "direct filtered     41.633133  5.150969  0.867175  6.412714  4.004853   \n",
       "\n",
       "                    std time  \n",
       "recursive full      0.014753  \n",
       "recursive dummy     0.004165  \n",
       "recursive filtered  0.009247  \n",
       "direct full         0.006484  \n",
       "direct dummy        0.005219  \n",
       "direct filtered     0.014758  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### SHOW RESULTS IN TABULAR FORMAT ###\n",
    "\n",
    "results = pd.DataFrame(results).T\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8fe19f6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:41:47.245944Z",
     "iopub.status.busy": "2022-08-26T06:41:47.245090Z",
     "iopub.status.idle": "2022-08-26T06:41:47.771572Z",
     "shell.execute_reply": "2022-08-26T06:41:47.770468Z"
    },
    "papermill": {
     "duration": 0.574199,
     "end_time": "2022-08-26T06:41:47.774087",
     "exception": false,
     "start_time": "2022-08-26T06:41:47.199888",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT FORECASTING ERRORS ###\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "\n",
    "plt.subplot(221)\n",
    "res = results['mse'].sort_values()\n",
    "res.plot(\n",
    "    kind='barh', title='MSE',\n",
    "    color=['orange' if 'recursive' in r else 'blue' for r in res.index]\n",
    ")\n",
    "\n",
    "plt.subplot(222)\n",
    "res = results['mae'].sort_values()\n",
    "res.plot(\n",
    "    kind='barh', title='MAE',\n",
    "    color=['orange' if 'recursive' in r else 'blue' for r in res.index],\n",
    ")\n",
    "\n",
    "plt.subplot(223)\n",
    "res = results['mape'].sort_values()\n",
    "res.plot(\n",
    "    kind='barh', title='MAPE',\n",
    "    color=['orange' if 'recursive' in r else 'blue' for r in res.index],\n",
    ")\n",
    "\n",
    "plt.subplot(224)\n",
    "res = results['rmse'].sort_values()\n",
    "res.plot(\n",
    "    kind='barh', title='RMSE',\n",
    "    color=['orange' if 'recursive' in r else 'blue' for r in res.index],\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "54eba823",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T06:41:47.865258Z",
     "iopub.status.busy": "2022-08-26T06:41:47.864874Z",
     "iopub.status.idle": "2022-08-26T06:41:48.004083Z",
     "shell.execute_reply": "2022-08-26T06:41:48.003085Z"
    },
    "papermill": {
     "duration": 0.18849,
     "end_time": "2022-08-26T06:41:48.006716",
     "exception": false,
     "start_time": "2022-08-26T06:41:47.818226",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT INFERENCE TIME ###\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "res = results['tot time'].sort_values()\n",
    "res.plot(\n",
    "    kind='barh', title='forecasting time',\n",
    "    color=['orange' if 'recursive' in r else 'blue' for r in res.index]\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 480.065248,
   "end_time": "2022-08-26T06:41:50.674044",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-08-26T06:33:50.608796",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
